Speeded up descriptors evaluations using clear ()

modules/features2d/include/opencv2/features2d/features2d.hpp

@@ -1024,13 +1024,14 @@ public:
                          const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
                          int pca_dim_high = 100, int pca_dim_low = 100);
 
-
     OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename, const string &train_path = string(), const string &images_list = string(),
-                         int pyr_levels = 1,
+                         float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
                          int pca_dim_high = 100, int pca_dim_low = 100);
 
 
     virtual ~OneWayDescriptorBase();
+    void clear ();
+
 
     // Allocate: allocates memory for a given number of descriptors
     void Allocate(int train_feature_count);
@@ -1124,7 +1125,7 @@ public:
     // GeneratePCA: calculate and save PCA components and descriptors
     // - img_path: path to training PCA images directory
     // - images_list: filename with filenames of training PCA images
-    void GeneratePCA(const char* img_path, const char* images_list);
+    void GeneratePCA(const char* img_path, const char* images_list, int pose_count=500);
 
     // SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
     void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);
@@ -1168,6 +1169,9 @@ protected:
     int m_pca_dim_low;
 
     int m_pyr_levels;
+    const float scale_min;
+    const float scale_max;
+    const float scale_step;
 };
 
 class CV_EXPORTS OneWayDescriptorObject : public OneWayDescriptorBase
@@ -1184,9 +1188,9 @@ public:
     OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
                            const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);
 
-
     OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
-                           const string &train_path = string (), const string &images_list = string (), int pyr_levels = 1);
+                           const string &train_path = string (), const string &images_list = string (),
+                           float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1);
 
 
     virtual ~OneWayDescriptorObject();
@@ -1705,9 +1709,9 @@ public:
         static const int POSE_COUNT = 500;
         static const int PATCH_WIDTH = 24;
         static const int PATCH_HEIGHT = 24;
-        static float GET_MIN_SCALE() { return 1.f; }
-        static float GET_MAX_SCALE() { return 3.f; }
-        static float GET_STEP_SCALE() { return 1.15f; }
+        static float GET_MIN_SCALE() { return 0.7f; }
+        static float GET_MAX_SCALE() { return 1.5f; }
+        static float GET_STEP_SCALE() { return 1.2f; }
 
         Params( int _poseCount = POSE_COUNT,
                 Size _patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
@@ -1755,6 +1759,8 @@ public:
     // Classify a set of keypoints. The same as match, but returns point classes rather than indices
     virtual void classify( const Mat& image, vector<KeyPoint>& points );
 
+    virtual void clear ();
+
 protected:
     Ptr<OneWayDescriptorBase> base;
     Params params;

modules/features2d/src/descriptors.cpp

@@ -204,7 +204,7 @@ void OneWayDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
 {
     if( base.empty() )
         base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
-                                           params.trainPath, params.trainImagesList);
+                                           params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
 
     size_t trainFeatureCount = keypoints.size();
 
@@ -225,7 +225,7 @@ void OneWayDescriptorMatch::add( KeyPointCollection& keypoints )
 {
     if( base.empty() )
         base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
-                                           params.trainPath, params.trainImagesList);
+                                           params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
 
     size_t trainFeatureCount = keypoints.calcKeypointCount();
 
@@ -275,6 +275,12 @@ void OneWayDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& points
     }
 }
 
+void OneWayDescriptorMatch::clear ()
+{
+    GenericDescriptorMatch::clear();
+    base->clear ();
+}
+
 /****************************************************************************************\
 *                                CalonderDescriptorMatch                                 *
 \****************************************************************************************/

modules/features2d/src/oneway.cpp

@@ -1213,7 +1213,8 @@ namespace cv{
     OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path,
                                                const char* pca_config, const char* pca_hr_config,
                                                const char* pca_desc_config, int pyr_levels,
-                                               int pca_dim_high, int pca_dim_low) : m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low)
+                                               int pca_dim_high, int pca_dim_low)
+    : m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low), scale_min (0.7f), scale_max(1.5f), scale_step (1.2f)
     {
         //	m_pca_descriptors_matrix = 0;
         m_patch_size = patch_size;
@@ -1270,8 +1271,10 @@ namespace cv{
     }
 
     OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename,
-                                               const string &train_path, const string &images_list, int pyr_levels,
-                                               int pca_dim_high, int pca_dim_low) : m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low)
+                                               const string &train_path, const string &images_list, float _scale_min, float _scale_max,
+                                               float _scale_step, int pyr_levels,
+                                               int pca_dim_high, int pca_dim_low)
+    : m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low), scale_min(_scale_min), scale_max(_scale_max), scale_step(_scale_step)
     {
         //  m_pca_descriptors_matrix = 0;
         m_patch_size = patch_size;
@@ -1341,6 +1344,20 @@ namespace cv{
 #endif
     }
     
+    void OneWayDescriptorBase::clear(){
+        delete []m_descriptors;
+        m_descriptors = 0;
+
+#if defined(_KDTREE)
+        //      if (m_pca_descriptors_matrix)
+        //              delete m_pca_descriptors_matrix;
+        cvReleaseMat(&m_pca_descriptors_matrix);
+        m_pca_descriptors_matrix = 0;
+        delete m_pca_descriptors_tree;
+        m_pca_descriptors_tree = 0;
+#endif
+    }
+
     void OneWayDescriptorBase::InitializePoses()
     {
         m_poses = new CvAffinePose[m_pose_count];
@@ -1423,25 +1440,25 @@ namespace cv{
 #if 0
         ::FindOneWayDescriptor(m_train_feature_count, m_descriptors, patch, desc_idx, pose_idx, distance, m_pca_avg, m_pca_eigenvectors);
 #else
-        float scale_min = 0.7f;
-        float scale_max = 2.0f;
-        float scale_step = 1.2f;
+        float min = scale_min;
+        float max = scale_max;
+        float step = scale_step;
         
         if (scale_ranges)
         {
-            scale_min = scale_ranges[0];
-            scale_max = scale_ranges[1];
+            min = scale_ranges[0];
+            max = scale_ranges[1];
         }
         
         float scale = 1.0f;
         
 #if !defined(_KDTREE)
         cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch,
-                                   scale_min, scale_max, scale_step, desc_idx, pose_idx, distance, scale,
+                                   min, max, step, desc_idx, pose_idx, distance, scale,
                                    m_pca_avg, m_pca_eigenvectors);
 #else
         cv::FindOneWayDescriptorEx(m_pca_descriptors_tree, m_descriptors[0].GetPatchSize(), m_descriptors[0].GetPCADimLow(), m_pose_count, patch,
-                                   scale_min, scale_max, scale_step, desc_idx, pose_idx, distance, scale,
+                                   min, max, step, desc_idx, pose_idx, distance, scale,
                                    m_pca_avg, m_pca_eigenvectors);
 #endif
         
@@ -1454,14 +1471,14 @@ namespace cv{
     void OneWayDescriptorBase::FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
                                               std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges) const
     {
-        float scale_min = 0.7f;
-        float scale_max = 2.5f;
-        float scale_step = 1.2f;
+        float min = scale_min;
+        float max = scale_max;
+        float step = scale_step;
         
         if (scale_ranges)
         {
-            scale_min = scale_ranges[0];
-            scale_max = scale_ranges[1];
+            min = scale_ranges[0];
+            max = scale_ranges[1];
         }
         
         distances.resize(n);
@@ -1471,7 +1488,7 @@ namespace cv{
         /*float scales = 1.0f;*/
         
         cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch,
-                                   scale_min, scale_max, scale_step ,n, desc_idxs, pose_idxs, distances, _scales,
+                                   min, max, step ,n, desc_idxs, pose_idxs, distances, _scales,
                                    m_pca_avg, m_pca_eigenvectors);
         
     }
@@ -1719,7 +1736,7 @@ namespace cv{
         calcPCAFeatures(patches, fs, postfix, avg, eigenvectors);
     }
 
-    void OneWayDescriptorBase::GeneratePCA(const char* img_path, const char* images_list)
+    void OneWayDescriptorBase::GeneratePCA(const char* img_path, const char* images_list, int pose_count)
     {
         char pca_filename[1024];
         sprintf(pca_filename, "%s/%s", img_path, GetPCAFilename().c_str());
@@ -1729,7 +1746,6 @@ namespace cv{
         generatePCAFeatures(img_path, images_list, fs, "lr", cvSize(m_patch_size.width / 2, m_patch_size.height / 2),
                             &m_pca_avg, &m_pca_eigenvectors);
 
-        const int pose_count = 500;
         OneWayDescriptorBase descriptors(m_patch_size, pose_count);
         descriptors.SetPCAHigh(m_pca_hr_avg, m_pca_hr_eigenvectors);
         descriptors.SetPCALow(m_pca_avg, m_pca_eigenvectors);
@@ -1926,13 +1942,12 @@ namespace cv{
     }
     
     OneWayDescriptorObject::OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
-                                                   const string &train_path, const string &images_list, int pyr_levels) :
-    OneWayDescriptorBase(patch_size, pose_count, pca_filename, train_path, images_list, pyr_levels)
+                                                   const string &train_path, const string &images_list, float _scale_min, float _scale_max, float _scale_step, int pyr_levels) :
+    OneWayDescriptorBase(patch_size, pose_count, pca_filename, train_path, images_list, _scale_min, _scale_max, _scale_step, pyr_levels)
     {
         m_part_id = 0;
     }
 
-
     OneWayDescriptorObject::~OneWayDescriptorObject()
     {
         delete []m_part_id;

tests/cv/src/adetectordescriptor_evaluation.cpp

@@ -1461,6 +1461,7 @@ const string PRECISION = "precision";
 const string KEYPOINTS_FILENAME = "keypointsFilename";
 const string PROJECT_KEYPOINTS_FROM_1IMAGE = "projectKeypointsFrom1Image";
 const string MATCH_FILTER = "matchFilter";
+const string RUN_PARAMS_IS_IDENTICAL = "runParamsIsIdentical";
 
 const string ONE_WAY_TRAIN_DIR = "detectors_descriptors_evaluation/one_way_train_images/";
 const string ONE_WAY_IMAGES_LIST = "one_way_train_images.txt";
@@ -1514,6 +1515,7 @@ protected:
         string keypontsFilename;
         bool projectKeypointsFrom1Image;
         int matchFilter; // not used now
+        bool runParamsIsIdentical;
     };
     vector<CommonRunParams> commRunParams;
 };
@@ -1565,6 +1567,7 @@ void DescriptorQualityTest::readDatasetRunParams( FileNode& fn, int datasetIdx )
     commRunParams[datasetIdx].keypontsFilename = (string)fn[KEYPOINTS_FILENAME];
     commRunParams[datasetIdx].projectKeypointsFrom1Image = (int)fn[PROJECT_KEYPOINTS_FROM_1IMAGE] != 0;
     commRunParams[datasetIdx].matchFilter = (int)fn[MATCH_FILTER];
+    commRunParams[datasetIdx].runParamsIsIdentical = (int)fn[RUN_PARAMS_IS_IDENTICAL];
 }
 
 void DescriptorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
@@ -1572,6 +1575,7 @@ void DescriptorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetI
     fs << KEYPOINTS_FILENAME << commRunParams[datasetIdx].keypontsFilename;
     fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParams[datasetIdx].projectKeypointsFrom1Image;
     fs << MATCH_FILTER << commRunParams[datasetIdx].matchFilter;
+    fs << RUN_PARAMS_IS_IDENTICAL << commRunParams[datasetIdx].runParamsIsIdentical;
 }
 
 void DescriptorQualityTest::setDefaultDatasetRunParams( int datasetIdx )
@@ -1579,6 +1583,7 @@ void DescriptorQualityTest::setDefaultDatasetRunParams( int datasetIdx )
     commRunParams[datasetIdx].keypontsFilename = "surf_" + DATASET_NAMES[datasetIdx] + ".xml.gz";
     commRunParams[datasetIdx].projectKeypointsFrom1Image = true;
     commRunParams[datasetIdx].matchFilter = NO_MATCH_FILTER;
+    commRunParams[datasetIdx].runParamsIsIdentical = true;
 }
 
 void DescriptorQualityTest::run( int )
@@ -1586,6 +1591,8 @@ void DescriptorQualityTest::run( int )
     readAllDatasetsRunParams();
     readResults();
 
+    Ptr<GenericDescriptorMatch> descMatch;
+
     int notReadDatasets = 0;
     int progress = 0, progressCount = DATASETS_COUNT*TEST_CASE_COUNT;
     for(int di = 0; di < DATASETS_COUNT; di++ )
@@ -1608,6 +1615,12 @@ void DescriptorQualityTest::run( int )
             vector<KeyPoint> keypoints1; vector<EllipticKeyPoint> ekeypoints1;
             readKeypoints( keypontsFS, keypoints1, 0);
             transformToEllipticKeyPoints( keypoints1, ekeypoints1 );
+
+            if (!commRunParams[di].runParamsIsIdentical)
+            {
+                descMatch = createDescriptorMatch (di);
+            }
+
             for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
             {
                 progress = update_progress( progress, di*TEST_CASE_COUNT + ci, progressCount, 0 );
@@ -1624,7 +1637,6 @@ void DescriptorQualityTest::run( int )
                     readKeypoints( keypontsFS, keypoints2, ci+1 );
                 transformToEllipticKeyPoints( keypoints2, ekeypoints2 );
 
-                Ptr<GenericDescriptorMatch> descMatch = createDescriptorMatch(di);
                 descMatch->add( imgs[ci+1], keypoints2 );
                 vector<int> matches1to2;
                 descMatch->match( imgs[0], keypoints1, matches1to2 );
@@ -1635,6 +1647,8 @@ void DescriptorQualityTest::run( int )
                                      correctMatchCount, falseMatchCount, correspCount );
                 calcQuality[di][ci].recall = recall( correctMatchCount, correspCount );
                 calcQuality[di][ci].precision = precision( correctMatchCount, falseMatchCount );
+
+                descMatch->clear ();
             }
         }
     }